Changes in Neuropeptide Prohormone Genes among Cetartiodactyla Livestock and Wild Species Associated with Evolution and Domestication

A Comprehensive Systematic Review Coupled with an Interacting Network Analysis Identified Candidate Genes and Biological Pathways Related to Bovine Temperament

Comprehension of the genetic basis of temperament has been improved by recent advances in the identification of genes and genetic variants. However, due to the complexity of the temperament traits, the elucidation of the genetic architecture of temperament is incomplete. A systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to analyze candidate genes related to bovine temperament, using bovine as the population, SNPs and genes as the exposure, and temperament test as the outcome, as principal search terms for population, exposure, and outcome (PEO) categories to define the scope of the search. The search results allowed the selection of 36 articles after removing duplicates and filtering by relevance. One hundred-two candidate genes associated with temperament traits were identified. The genes were further analyzed to construct an interaction network using the STRING database, resulting in 113 nodes and 346 interactions and the identification of 31 new candidate genes for temperament. Notably, the main genes identified were SST and members of the Kelch family. The candidate genes displayed interactions with pathways associated with different functions such as AMPA receptors, hormones, neuronal maintenance, protein signaling, neuronal regulation, serotonin synthesis, splicing, and ubiquitination activities. These new findings demonstrate the complexity of interconnected biological processes that regulate behavior and stress response in mammals. This insight now enables our targeted analysis of these newly identified temperament candidate genes in bovines.

Bioinformatics for Prohormone and Neuropeptide Discovery

Neuropeptides and peptide hormones are signaling molecules produced via complex posttranslational modifications of precursor proteins known as prohormones. Neuropeptides activate specific receptors and are associated with the regulation of physiological systems and behaviors. The identification of prohormones-and the neuropeptides created by these prohormones-from genomic assemblies has become essential to support the annotation and use of the rapidly growing number of sequenced genomes. Here we describe a well-validated methodology for identifying the prohormone complement from genomic assemblies that employs widely available public toolsets and databases. The uncovered prohormone sequences can then be screened for putative neuropeptides to enable accurate proteomic discovery and validation.